---
base_model: cambridgeltl/SapBERT-from-PubMedBERT-fulltext
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: exorphins
- text: phosphatidylethanolamines
- text: lipopolysaccharides
- text: ion channels
- text: caspases
inference: false
model-index:
- name: SetFit with cambridgeltl/SapBERT-from-PubMedBERT-fulltext
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.17570754716981132
      name: Accuracy
---

# SetFit with cambridgeltl/SapBERT-from-PubMedBERT-fulltext

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [cambridgeltl/SapBERT-from-PubMedBERT-fulltext](https://huggingface.co/cambridgeltl/SapBERT-from-PubMedBERT-fulltext) as the Sentence Transformer embedding model. A MultiOutputClassifier instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

## Model Details

### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [cambridgeltl/SapBERT-from-PubMedBERT-fulltext](https://huggingface.co/cambridgeltl/SapBERT-from-PubMedBERT-fulltext)
- **Classification head:** a MultiOutputClassifier instance
- **Maximum Sequence Length:** 512 tokens
<!-- - **Number of Classes:** Unknown -->
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.1757   |

## Uses

### Direct Use for Inference

First install the SetFit library:

```bash
pip install setfit
```

Then you can load this model and run inference.

```python
from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("caspases")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 1.7652 | 5   |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (3, 3)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 15
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0009 | 1    | 0.2361        | -               |
| 0.0463 | 50   | 0.2377        | -               |
| 0.0927 | 100  | 0.2269        | -               |
| 0.1390 | 150  | 0.2104        | -               |
| 0.1854 | 200  | 0.1871        | -               |
| 0.2317 | 250  | 0.1437        | -               |
| 0.2780 | 300  | 0.1322        | -               |
| 0.3244 | 350  | 0.1365        | -               |
| 0.3707 | 400  | 0.1155        | -               |
| 0.4171 | 450  | 0.1144        | -               |
| 0.4634 | 500  | 0.1068        | -               |
| 0.5097 | 550  | 0.1011        | -               |
| 0.5561 | 600  | 0.095         | -               |
| 0.6024 | 650  | 0.0933        | -               |
| 0.6487 | 700  | 0.1063        | -               |
| 0.6951 | 750  | 0.0999        | -               |
| 0.7414 | 800  | 0.0823        | -               |
| 0.7878 | 850  | 0.0877        | -               |
| 0.8341 | 900  | 0.0767        | -               |
| 0.8804 | 950  | 0.0849        | -               |
| 0.9268 | 1000 | 0.0796        | -               |
| 0.9731 | 1050 | 0.0877        | -               |
| 1.0195 | 1100 | 0.0759        | -               |
| 1.0658 | 1150 | 0.0705        | -               |
| 1.1121 | 1200 | 0.0728        | -               |
| 1.1585 | 1250 | 0.0738        | -               |
| 1.2048 | 1300 | 0.0767        | -               |
| 1.2512 | 1350 | 0.0692        | -               |
| 1.2975 | 1400 | 0.0697        | -               |
| 1.3438 | 1450 | 0.0639        | -               |
| 1.3902 | 1500 | 0.0729        | -               |
| 1.4365 | 1550 | 0.0759        | -               |
| 1.4829 | 1600 | 0.0786        | -               |
| 1.5292 | 1650 | 0.0618        | -               |
| 1.5755 | 1700 | 0.0722        | -               |
| 1.6219 | 1750 | 0.0719        | -               |
| 1.6682 | 1800 | 0.072         | -               |
| 1.7146 | 1850 | 0.0654        | -               |
| 1.7609 | 1900 | 0.0683        | -               |
| 1.8072 | 1950 | 0.0654        | -               |
| 1.8536 | 2000 | 0.0679        | -               |
| 1.8999 | 2050 | 0.0643        | -               |
| 1.9462 | 2100 | 0.0662        | -               |
| 1.9926 | 2150 | 0.0642        | -               |
| 2.0389 | 2200 | 0.0812        | -               |
| 2.0853 | 2250 | 0.068         | -               |
| 2.1316 | 2300 | 0.0583        | -               |
| 2.1779 | 2350 | 0.0627        | -               |
| 2.2243 | 2400 | 0.0654        | -               |
| 2.2706 | 2450 | 0.0571        | -               |
| 2.3170 | 2500 | 0.0623        | -               |
| 2.3633 | 2550 | 0.0639        | -               |
| 2.4096 | 2600 | 0.059         | -               |
| 2.4560 | 2650 | 0.0637        | -               |
| 2.5023 | 2700 | 0.0675        | -               |
| 2.5487 | 2750 | 0.0696        | -               |
| 2.5950 | 2800 | 0.0669        | -               |
| 2.6413 | 2850 | 0.0633        | -               |
| 2.6877 | 2900 | 0.0606        | -               |
| 2.7340 | 2950 | 0.0609        | -               |
| 2.7804 | 3000 | 0.054         | -               |
| 2.8267 | 3050 | 0.0598        | -               |
| 2.8730 | 3100 | 0.0597        | -               |
| 2.9194 | 3150 | 0.0618        | -               |
| 2.9657 | 3200 | 0.065         | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.1.1
- Transformers: 4.39.0
- PyTorch: 2.4.1+cu121
- Datasets: 3.0.0
- Tokenizers: 0.15.2

## Citation

### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}
```

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